Neural networks refer to a computational architecture modeled after biological brains. Within a neural network, nodes referred to as neurons may be interconnected and operate collectively to process input data. Examples of different types of neural networks include, but are not limited to, Convolutional Neural Networks, Recurrent Neural Networks, Deep Belief Networks, Restricted Boltzman Machines, etc. In a feedforward neural network, the neurons of the neural network have links to other neurons. The links only extend in one direction, i.e., the forward direction, through the neural network.
A neural network may be used to extract “features” from complex input data. The neural network may include a plurality of layers. Each layer may receive input data and generate output data by processing the input data to the layer. The output data may be a feature map of the input data that the neural network generates by convolving an input image or a feature map with convolution kernels. Initial layers of a neural network may be operative to extract low level features such as edges and/or gradients from an input such as an image. Subsequent layers of the neural network may extract progressively more complex features such as eyes, a nose, or the like.